Stochastic Programming Problems with Recourse via Empirical Estimates

Vlasta Kaňková ()
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Vlasta Kaňková: Academy of Sciences of the Czech Republic, Institute of Information Theory and Automation

Chapter 57 in Operations Research Proceedings 2008, 2009, pp 351-356 from Springer

Abstract: Summary Let ξ := ξ (ω) (s×1) be a random vector defined on a probability space (Ω; S; P); F; PF the distribution function and the probability measure corresponding to the random vector ξ : Let, moreover, $$g{\rm o}(x,z),g_0^1 (y,z)$$ be functions defined on Rn × Rs and Rn1 × Rs; fi(x; z); gi(y); i = 1; : : : ; m functions defined on Rn × Rs and Rn1 ; h := h(z) (m × 1) a vector function defined on Rs; $$h'(z) = (h_1 (z), \ldots ,h_m (z));X \subset R^n ,Y \subset R^{n1}$$ be nonempty sets. Symbols x (n × 1); y := y (x; ξ) (n1 × 1) denote decision vectors. (Rn denotes the n-dimensional Euclidean space, h' a transposition of the vector function h:)

Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-00142-0_57

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DOI: 10.1007/978-3-642-00142-0_57

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